Pedestrian detection is one of the most active research topics in the fields of pattern recognition and machine learning. It has been widely used in intelligent monitoring, auxiliary driving and so on. Generating pedestrian detection proposals is an important work in the early period of pedestrian recognition and pedestrian tracking. Based on the static monitoring scene as well as the on-board monitoring scene under specific circumstances, a novel method to generate pedestrian detection proposals quickly (OL_GMPG) is proposed by using online Gaussian model. High detection rate can be achieved by generating fewer pedestrian detection proposals through the Gaussian model fitting. Both the positions where people appear most frequently and the scale information of corresponding targets can be obtained through the learning and updating processes of the Gaussian model. The information is beneficial to subsequent pedestrian recognition or pedestrian tracking.